Superspreading and the effect of individual variation on disease emergence.
Superspreading and the effect of individual variation on disease emergence.
复制标题
DOI：
10.1038/nature04153
复制
发表时间：
2005-11-17
期刊：
影响因子：
64.8
通讯作者：
Getz WM
中科院分区：
文献类型：
--
作者：
Lloyd-Smith JO;Schreiber SJ;Kopp PE;Getz WM
从伤寒玛丽到SARS，长期以来，有些人比其他人传播的疾病更多。疾病的传播潜力。致命的疾病。增长但与传统的基于平均的观点有明显不同的观点。
本文的在线版本（doi：10.1038/nature04153）包含补充材料，可供授权用户使用。
人口级别的分析通常使用平均数量来描述异质系统，尤其是当可识别的群体不引起差异时，这是我们当前对流行病的理解的重要例子，是基本的复制数字R0，它被定义为平均值。在易感人群中，由感染者引起的感染数量估计R0的人群会掩盖感染的大量差异许多“超级宣传事件”的严重急性呼吸综合症（SARS）的全球出现，其中某些人会感染大量的次要病例。从经验上进行衡量，因此在这里尚不清楚其对爆发动态的重要性。感染性的影响对疾病出现的影响。疾病的延伸可能性更大，爆发更罕见，但使用这些模型更具爆炸性。特定的控制措施超过了人口范围的测量。此外，通过有针对性的控制策略实现的显着改善，强调了确定较高感染率的预测性相关性的需求。我们为超级广播事件提出了一个严格的定义，并提出了一种预测其频率的方法。
本文的在线版本（doi：10.1038/nature04153）包含补充材料，可供授权用户使用。
From Typhoid Mary to SARS, it has long been known that some people spread disease more than others. But for diseases transmitted via casual contact, contagiousness arises from a plethora of social and physiological factors, so epidemiologists have tended to rely on population averages to assess a disease's potential to spread. A new analysis of outbreak data shows that individual differences in infectiousness exert powerful influences on the epidemiology of ten deadly diseases. SARS and measles (and perhaps avian influenza) show strong tendencies towards ‘superspreading events’ that can ignite explosive epidemics — but this same volatility makes outbreaks more likely to fizzle out. Smallpox and pneumonic plague, two potential bioterrorism agents, show steadier growth but still differ markedly from the traditional average-based view. These findings are relevant to how emerging diseases are detected and controlled. The online version of this article (doi:10.1038/nature04153) contains supplementary material, which is available to authorized users. Population-level analyses often use average quantities to describe heterogeneous systems, particularly when variation does not arise from identifiable groups. A prominent example, central to our current understanding of epidemic spread, is the basic reproductive number, R0, which is defined as the mean number of infections caused by an infected individual in a susceptible population. Population estimates of R0 can obscure considerable individual variation in infectiousness, as highlighted during the global emergence of severe acute respiratory syndrome (SARS) by numerous ‘superspreading events’ in which certain individuals infected unusually large numbers of secondary cases. For diseases transmitted by non-sexual direct contacts, such as SARS or smallpox, individual variation is difficult to measure empirically, and thus its importance for outbreak dynamics has been unclear. Here we present an integrated theoretical and statistical analysis of the influence of individual variation in infectiousness on disease emergence. Using contact tracing data from eight directly transmitted diseases, we show that the distribution of individual infectiousness around R0 is often highly skewed. Model predictions accounting for this variation differ sharply from average-based approaches, with disease extinction more likely and outbreaks rarer but more explosive. Using these models, we explore implications for outbreak control, showing that individual-specific control measures outperform population-wide measures. Moreover, the dramatic improvements achieved through targeted control policies emphasize the need to identify predictive correlates of higher infectiousness. Our findings indicate that superspreading is a normal feature of disease spread, and to frame ongoing discussion we propose a rigorous definition for superspreading events and a method to predict their frequency. The online version of this article (doi:10.1038/nature04153) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
11.8
作者：
Gani, R;Leach, S
通讯作者：
Leach, S
影响因子：
56.9
作者：
Lipsitch, M;Cohen, T;Murray, M
通讯作者：
Murray, M
影响因子：
2.1
作者：
Farrington, CP;Kanaan, MN;Gay, NJ
通讯作者：
Gay, NJ
影响因子：
5
作者：
Wallinga, J;Teunis, P
通讯作者：
Teunis, P
影响因子：
11.8
作者：
Shen Z;Ning F;Zhou W;He X;Lin C;Chin DP;Zhu Z;Schuchat A
通讯作者：
Schuchat A